Wednesday, March 16, 2016

So, What Will Google’s Winning Go Algorithm Do Now That It's Won the Million Bucks?

Probably not the hookers and blow you see in the less cerebral/more raucous world of high-stakes chess.

From the journal Nature:

AlphaGo’s techniques could have broad uses, but moving beyond games is a challenge.

Following the defeat of one of its finest human players, the ancient
game of Go has joined the growing list of tasks at which computers
perform better than humans. In a 6-day tournament in Seoul, watched by a
reported 100 million people around the world, the computer algorithm
AlphaGo, created by the Google-owned company DeepMind, beat Go
professional Lee Sedol by 4 games to 1. The complexity and intuitive
nature of the ancient board game had established Go as one the greatest
challenges in artificial intelligence (AI). Now the big question is what
the DeepMind team will turn to next.

AlphaGo’s general-purpose approach — which was mainly
learned, with a few elements crafted specifically for the game — could
be applied to problems that involve pattern recognition, decision-making
and planning. But the approach is also limited. “It’s really
impressive, but at the same time, there are still a lot of challenges,”
says Yoshua Bengio, a computer scientist at the University of Montreal
in Canada.

Lee, who had predicted that he would win the Google tournament in a landslide, was shocked by his loss. In October, AlphaGo beat European champion Fan Hui.
But the version of the program that won in Seoul is significantly
stronger, says Jonathan Schaeffer, a computer scientist at the
University of Alberta in Edmonton, Canada, whose Chinook software
mastered draughts in 2007: “I expected them to use more computational
resources and do a lot more learning, but I still didn’t expect to see
this amazing level of performance.”

The
improvement was largely down to the fact that the more AlphaGo plays,
the better it gets, says Miles Brundage, a social scientist at Arizona
State University in Tempe, who studies trends in AI. AlphaGo uses a brain-inspired architecture
known as a neural network, in which connections between layers of
simulated neurons strengthen on the basis of experience. It learned by
first studying 30 million Go positions from human games and then
improving by playing itself over and over again, a technique known as
reinforcement learning. Then, DeepMind combined AlphaGo’s ability to
recognize successful board configurations with a ‘look-ahead search’, in
which it explores the consequences of playing promising moves and uses
that to decide which one to pick.

Next, DeepMind could tackle more games. Most board games, in which
players tend to have access to all information about play, are now
solved. But machines still cannot beat humans at multiplayer poker, say,
in which each player sees only their own cards. The DeepMind team has
expressed an interest in tackling Starcraft, a science-fiction strategy
game, and Schaeffer suggests that DeepMind devise a program that can
learn to play different types of game from scratch. Such programs
already compete annually at the International General Game Playing
Competition, which is geared towards creating a more general type of AI.
Schaeffer suspects that DeepMind would excel at the contest. “It’s so
obvious, that I’m positive they must be looking at it,” he says....MORE